@article {Abeliansky2020,
	title = {The Future of Work: Challenges for Job Creation Due to Global Demographic Change and Automation},
	journal = {IZA Discussion Paper Series},
	number = {12962},
	year = {2020},
	keywords = {demography, labor, unemployment},
	url = {www.iza.org},
	author = {Abeliansky, Ana and Algur, Eda and Bloom, David E and Prettner, Klaus}
}
@article {EganadelSol2020,
	title = {The Future of Work in Developing Economies},
	journal = {MIT Sloan Management Review},
	volume = {61},
	number = {2},
	year = {2020},
	pages = {1{\textendash}3},
	abstract = {<p>Much has been written about the rise of automation in developed countries. Economists have been busily creating models seeking to quantify the likely impact of automation on employment. However, far less has been written about the potential effects on work in developing nations. This is surprising, given that automation may be especially troublesome for developing economies. Here, del Sol and Joyce examine the effects of large-scale automation on workers in developing countries.</p>},
	keywords = {Armenia, Asia, Austria, automation, Bolivia, Business And Economics{\textendash}Management, China, Developing countries{\textendash}LDCs, Employment, future, Georgia (country), Ghana, Impact analysis, Kenya, Kuala Lumpur Malaysia, Laos, Republic of North Macedonia, South Korea, Sri Lanka, United States{\textendash}US, Vietnam, Workers},
	issn = {15329194},
	author = {Egana del Sol, Pablo and Joyce, Connor and Del Sol, Pablo Ega{\~n}a and Joyce, Connor}
}
@article {2019,
	title = {Digitalization and the future of work: Macroeconomic consequences},
	year = {2019},
	publisher = {ZEW - Leibniz-Zentrum f{\"u}r Europ{\"a}ische Wirtschaftsforschung},
	abstract = {Computing power continues to grow at an enormous rate. Simultaneously, more and better data is increasingly available and Machine Learning methods have seen significant breakthroughs in the recent past. All this pushes further the boundary of what machines can do. Nowadays increasingly complex tasks are automatable at a precision which seemed infeasible only few years ago. The examples range from voice and image recognition, playing Go, to self-driving vehicles. Machines are able to perform more and more manual and also cognitive tasks that previously only humans could do. As a result of these developments, some argue that large shares of jobs are "at risk of automation", spurring public fears of massive job-losses and technological unemployment. This chapter discusses how new digital technologies might affect the labor market in the near future. First, the chapter discusses estimates of automation potentials, showing that many estimates are severely upward biased because they ignore that workers in seemingly automatable occupations already take over hard-to-automate tasks. Secondly, it highlights that these numbers only refer to what theoretically could be automated and that this must not be equated with job-losses or employment effects - a mistake that is done often in the public debate. Thirdly, the chapter develops scenarios on how digitalization is likely to affect the German labor market in the next five years and derives implications for policy makers on how to shape the future of work. Germany is an interesting case to study, as it is a developed country at the technological frontier. In particular, the main challenge will not be the number, but the structure of jobs and the corresponding need for supply side adjustments to meet the shift in demand both within and between occupations and sectors.},
	keywords = {automation, Digitalization, Inequality, unemployment},
	url = {http://hdl.handle.net/10419/200063},
	author = {Melanie Arntz and Terry Gregory and Ulrich Zierahn}
}
@article {2018,
	title = {Occupational Classifications With A Machine Learning Approach},
	year = {2018},
	keywords = {administrative data, machine learning, occupational classifications, transaction data, UMETRICS},
	url = {https://www.iza.org/publications/dp/11738/occupational-classifications-a-machine-learning-approach},
	author = {Akina Ikudo and Julia Lane and Joseph Staudt and Bruce A. Weinberg}
}
@article {2018,
	title = {Robots and Organization Studies: Why Robots Might Not Want to Steal Your Job},
	journal = {Organization Studies},
	year = {2018},
	month = {04/2018},
	pages = {017084061876556},
	edition = {2},
	abstract = {A number of recent high-profile studies of robotics and artificial intelligence (or AI) in economics and sociology have predicted that many jobs will soon disappear due to automation, with few new ones replacing them. While techno-optimists and techno-pessimists contest whether a jobless future is a positive development or not, this paper points to the elephant in the room. Despite successive waves of computerization (including advanced machine learning), jobs have not disappeared. And probably won{\textquoteright}t in the near future. To explain why, some basic insights from organization studies can make a contribution. I propose the concept of {\textquoteright}bounded automation{\textquoteright} to demonstrate how organizational forces mould the application of technology in the employment sector. If work does not vanish in the age of AI, then poorly paid jobs will most certainly proliferate, I argue. Finally, a case is made for the scholarly community to engage with wider social justice concerns. This I term public organization studies.},
	keywords = {artificial intelligence, bounded automation, neoliberalism, public organization studies, Robotics, unemployment, work},
	issn = {0170-8406},
	doi = {10.1177/0170840618765568},
	url = {http://journals.sagepub.com/doi/10.1177/0170840618765568},
	author = {Fleming, Peter}
}
